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http://dx.doi.org/10.7843/kgs.2021.37.11.107

Effect of Learning Data on the Semantic Segmentation of Railroad Tunnel Using Deep Learning  

Ryu, Young-Moo (Korea Railroad Research Institute)
Kim, Byung-Kyu (Korea Railroad Research Institute)
Park, Jeongjun (Korea Railroad Research Institute)
Publication Information
Journal of the Korean Geotechnical Society / v.37, no.11, 2021 , pp. 107-118 More about this Journal
Abstract
Scan-to-BIM can be precisely mod eled by measuring structures with Light Detection And Ranging (LiDAR) and build ing a 3D BIM (Building Information Modeling) model based on it, but has a limitation in that it consumes a lot of manpower, time, and cost. To overcome these limitations, studies are being conducted to perform semantic segmentation of 3D point cloud data applying deep learning algorithms, but studies on how segmentation result changes depending on learning data are insufficient. In this study, a parametric study was conducted to determine how the size and track type of railroad tunnels constituting learning data affect the semantic segmentation of railroad tunnels through deep learning. As a result of the parametric study, the similar size of the tunnels used for learning and testing, the higher segmentation accuracy, and the better results when learning through a double-track tunnel than a single-line tunnel. In addition, when the training data is composed of two or more tunnels, overall accuracy (OA) and mean intersection over union (MIoU) increased by 10% to 50%, it has been confirmed that various configurations of learning data can contribute to efficient learning.
Keywords
Deep learning; Point cloud data; Railroad tunnel; Scan-to-BIM; Semantic segmentation;
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